19 papers · ranked by Valyu relevance
Maxim Sakharov, Heming Jia
Memetic algorithms achieve strong optimization performance by combining population-based global search with local refinement operators, yet their effectiveness critically depends on the design and management of memes. Local search strategies are typically handcrafted, problem-specific, and fixed prior to execution.…
Syue-Cian Lin, Wei-Yu Chiu, Chien-Feng Wu
Bilinear Matrix Inequalities (BMIs) are fundamental to control system design but are notoriously difficult to solve due to their nonconvexity. This study addresses BMI-based control optimization problems by adapting and integrating advanced evolutionary strategies. Specifically, a memetic Covariance Matrix Adaptation…
Alkis Sygkounas, Victor Aregbede, Amy Loutfi, Andreas Persson
Long-horizon embodied tasks require policies that execute many dependent actions before task success can be observed. Representing policies as executable control pro- grams (code-as-policy) enables their decision logic to be inspected and revised after rollout evaluation. Revised programs can then be executed and…
Yujun Zheng, Xinya Chen, Xueqin Lu, Weiguo Sheng + 1 more
—Emotional stress often has a significant effect on the working performance of staff, but this effect is commonly neglected in existing staff scheduling methods. We study a callcenter staff scheduling problem, which considers the evolution of work performance of staff under emotional stress. First, we present an…
Alejandro Gomez Cadavid, Pranav Chandarana, Sebastián V. Romero, Trautmann + 4 more
We introduce quantum-enhanced memetic tabu search (QE-MTS), a non-variational hybrid algorithm that achieves state-of-the-art scaling for the low-autocorrelation binary sequence (LABS) problem. By seeding the classical MTS with high-quality initial states from digitized counterdiabatic quantum optimization (DCQO), our…
Elif Radová Garajová, Miroslav Rada
An interval transportation problem represents a model for a transportation problem in which the values of supply, demand, and transportation costs are affected by uncertainty and can vary independently within given interval ranges. One of the main tasks of solving interval programming models is computing the best and…
Zihang Yuan, Wenbin Zhang, Chunjiang Zhang, Raffaele Bruno
This paper addresses the challenge of integrated optimization for storage space allocation and crane scheduling in Automated Storage and Retrieval Systems (AS/RSs) under the complex constraints of dual I/O ports and Shared Storage strategies. The scheduling of multi-shuttle cranes in such environments constitutes a…
Inès Benito, Johannes F. Lutzeyer, Benjamin Doerr
Baldwinian and Lamarckian evolution have existed for a long time in evolutionary algorithms (EAs) without ever dominating the academic literature or practical applications. In this work, we use modern empirical and theoretical methods to revisit Lamarckian and Baldwinian evolution and rigorously compare them with the…
Luis Javier Madrigal-Roca, John K. Kelly
Gene co-expression analyses identify “good” modules by a correlation criterion. However, standard pipelines detect modules with greedy algorithms that optimize other quantities and only measure correlation afterwards. We present a method called Genetic Clustering by Metric(GCM hereafter), an open-source Python tool…
Andrea Polo-Rodríguez, David R. Penas, Julio R. Banga
Parameter estimation is a central challenge in systems biology, particularly for large dynamic models described by nonlinear ordinary differential equations (ODEs). These global optimization problems exhibit landscapes which are topologically heterogeneous, often exhibiting a pathological mixture of stiff, smooth…
Ulrich Schmitt, Heming Jia
This article introduces Memetic/Metaphorical Digital Twins (MDTs) as a novel extension of Digital Twin typologies by twinning conceptual schemes, complementing Industrial, Human, and Cognitive Digital Twins. MDTs embed cultural, organizational, and semiotic knowledge into digital frameworks, enabling the recombination…
Authors not listed
Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Zhijun Teng, Liangcen Gu, Mingyang Sun, Mugang He
Inspired by the intricate group dynamics of wild gorilla populations, the Artificial Gorilla Troops Optimizer (GTO) represents a novel approach in swarm intelligence. Despite its effectiveness in performing global exploration, GTO is prone to early convergence and can easily become stuck in local optima, especially…
N. Jayalakshmi, K. Sakthivel
To achieve robust and user friendly software, it is crucial to make sure that Graphical User Interfaces (GUI) is of quality and reliable. The paper suggests a new method of Quasi-Oppositional Genetic Sparrow Search Algorithm (OOGSSA) of generating test cases efficiently in GUI. The ultimate goal is to have…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Ali Asghari, Mohammadhossein Mohammadi, Heming Jia
Metaheuristic algorithms are widely used to find optimal or near-optimal solutions for complex problems by taking inspiration from natural behaviors and processes. Although many different methods have been developed, a common problem in many of them is maintaining a good balance between exploration and exploitation and…
Vaibhav Mohanty, Anna Sappington, Eugene I. Shakhnovich, Bonnie Berger
Classical population genetics has largely relied on the same stochastic differential equations (SDEs) for over 60 years to describe evolutionary dynamics. However, these SDEs ignore the fact that phenotype heterogeneity and noise are ubiquitous in biological systems from bacteria to cancers. Here, we develop…
Zhimeng Zhou, Yang Nan, Minjie Mou, Yuntao Qian + 16 more
Artificial intelligence (AI) is increasingly permeating the drug development pipeline. Numerous algorithms for accelerating this multi-stage and multi-task process have been constructed, which depends heavily on expert design and labor-intensive task-specific optimization. Given that AI-driven acceleration of drug…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…